An important contribution of the Semantic Web is a new format of data representation called Resource Description Framework (RDF). In RDF, every piece of information is represented as a triple: subject-property-object. In general, subjects and objects can be shared between multiple RDF triples, and all triples can constitute a densely interlinked network. RDF becomes a very popular format of representing data on the web. As of September 2012, the last available data, more than 31 billions of triples exist on the web. In the paper, we propose a system - called T2R - for automatic acquisition of syntactic and semantic relations among terms from a plain text. These relations are expressed in the form of RDF triples. The proposed method is independent of any prior knowledge and domain specific patterns, and is applicable to any textual resources. The system implementing the approach is capable of identifying grammatical structure of an input sentence and analysing its semantics to generate meaningful RDF triples. We evaluate this approach by proving the quality of our results through case studies.
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